Table of Contents
Which Operation Is Fundamental in MapReduce Aggregation Tasks for Big Data?
Master MapReduce aggregation tasks for your Hadoop certification. Learn why grouping by keys and counting values is the fundamental operation for processing Big Data in MapReduce, Pig, and Hive.
Question
Which operation is fundamental in MapReduce aggregation tasks?
A. Deleting unused rows
B. Filtering records only
C. Sorting by alphabetical order
D. Grouping by keys and counting values
Answer
D. Grouping by keys and counting values
Explanation
In MapReduce, the fundamental mechanism for performing any aggregation task is grouping data by specific keys and computing or counting their associated values. During the Map phase, raw data is parsed and emitted as intermediate key-value pairs (e.g., extracting “Male” or “Female” as the key and “1” as the value). The framework then automatically shuffles and sorts this data, ensuring that all identical keys are grouped together before they reach the Reduce phase. Finally, the Reducer iterates over these grouped values, aggregating them (via counting, summing, averaging, etc.) to produce the final summarized metrics.
Options A (Deleting unused rows), B (Filtering records only), and C (Sorting by alphabetical order) represent data cleaning, selection, or ordering operations, but they do not inherently perform the mathematical summarization that defines an aggregation task.